Tropospheric delay is one of the main error sources in Global Navigation Satellite System (GNSS) Precise Point Positioning (PPP). Zenith Hydrostatic Delay (ZHD) accounts for 90% of the total delay. This research focuses on the improvements of ZHD from tropospheric models and real meteorological data on the PPP solution. Multi-GNSS PPP experiments are conducted using the datasets collected at Multi-GNSS Experiments (MGEX) network stations. The results show that the positioning accuracy of different GNSS PPP solutions using the meteorological data for ZHD correction can achieve an accuracy level of several millimetres. The average convergence time of a PPP solution for the BeiDou System (BDS), the Global Positioning System (GPS), Global Navigation Satellite System of Russia (GLONASS), BDS+GPS, and BDS+GPS+GLONASS+Galileo are 55·89 min, 25·88 min, 33·30 min, 20·50 min and 15·71 min, respectively. The results also show that atmospheric parameters provided by real meteorological data have little effect on the horizontal components of positioning compared to the meteorological model, while in the vertical component, the positioning accuracy is improved by 90·6%, 33·0%, 22·2% and 19·8% compared with the standard atmospheric model, University of New Brunswick (UNB3m) model, Global Pressure and Temperature (GPT) model, and Global Pressure and Temperature-2 (GPT2) model and the convergence times are decreased 51·2%, 32·8%, 32·5%, and 32·3%, respectively.

In Network RTK (Real-Time Kinematic) positioning, the multiple corrections from the reference stations, which constitute a network, are interpolated for the user location through appropriate interpolation models. There exist various methods to model spatial decorrelation errors from the tropospheric and ionospheric delay, which are the main contributors of the multiple corrections. Since tropospheric delay is largely affected by height differences, the heights of the multiple reference stations should be considered when selecting the appropriate interpolation methods. This work provides a comparative analysis of the different levels of performance of each height-related multiple correction interpolation method. In addition, this study proposes to add constraints to the conventional height-related interpolation methods that are derived from the characteristics of the tropospheric zenith delay variation over height. The actual Global Positioning System (GPS) observations are collected from selected reference station networks located in the USA for performance evaluation. As a result, the proposed solution yields improved vertical positioning accuracy by approximately 10% compared to the conventional interpolation methods for the selected networks.

The troposphere affects electromagnetic signal propagation causing signal path bending and the alteration of the electromagnetic wave velocity. Tropospheric delay can introduce a considerable error in satellite positioning if it is not properly estimated. The GPS signal delay can vary from 2 to 20 m depending on the elevation angles between the receiver and the satellite. Two basic types of delay prediction models exist. The first use surface meteorological parameters to estimate the value of the tropospheric delay, and the other models that do not require real-time meteorological input use average and seasonal variation data related to the receiver's latitude and day-of-year. This paper compares the performance of both types of model over a period of one year, comprising all seasons, to verify their accuracy over a longer period. The Saastamoinen model, known as one of the best performing prediction models, was taken as a reference and the global EGNOS model was used to check how the global estimates of the yearly averages of the meteorological parameters and their related seasonal variations comply with the real-time surface parameters.

Real-time and near real-time precise GPS positioning requires shorter GPS solution convergence time. Residual tropospheric delay, which exists as a result of the limitations of existing tropospheric correction models, is a limiting factor for quick GPS solution convergence. This paper proposes a new approach to tropospheric delay modelling, which overcomes the limitations of existing models. In this approach, the bulk of the tropospheric delay is accounted for using the NOAA-generated tropospheric correction model, while the residual tropospheric delay component is accounted for stochastically. First, the NOAA tropospheric correction model is used to generate daily time series of zenith total tropospheric delays (ZTDs) at ten IGS reference stations spanning North America for many days in 2006. The NOAA ZTDs are then compared with the new highly-accurate IGS tropospheric delay product to obtain daily residual time series at 5 minute intervals. Finally, the auto-covariance functions of the daily residual tropospheric delay series are estimated at each of the ten reference stations and then used to find the best empirical covariance function in the least squares sense. Of the three potential covariance functions examined, it is shown that the exponential cosine function gives the best fit most of the time, while the second-order Gauss-Markov model gives the worst fit. The first-order Gauss-Markov fits are close to those of the exponential cosine. Additionally, the model coefficients seem to be season independent, but change with geographical location.